WebMate: a personal agent for browsing and searching
AGENTS '98 Proceedings of the second international conference on Autonomous agents
A hybrid user model for news story classification
UM '99 Proceedings of the seventh international conference on User modeling
World Wide Web
Clustering the Users of Large Web Sites into Communities
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Wrapper induction for information extraction
Wrapper induction for information extraction
Web Usage Mining as a Tool for Personalization: A Survey
User Modeling and User-Adapted Interaction
Personalised hypermedia presentation techniques for improving online customer relationships
The Knowledge Engineering Review
Newsjunkie: providing personalized newsfeeds via analysis of information novelty
Proceedings of the 13th international conference on World Wide Web
World news finder: how we cope without the semantic web
AIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications
A framework for human-robot interaction
Proceedings of the 1st international conference on PErvasive Technologies Related to Assistive Environments
Web feed clustering and tagging aggregator using topological tree-based self-organizing maps
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
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This paper presents a system that integrates news from multiple sources on the Web and delivers in a personalized fashion to the reader. The presented service integrates automatic information extraction from various news sources and presentation of information according to the user’s interests. The system consists of source-specific information extraction programs (wrappers) that extract highlights of news items from the various sources, organize them according to pre-defined news categories and present them to the user through a personal Web-based interface. Dynamic personalization is used based on the user’s reading history, as well as the preferences of other similar users. User models are maintained by statistical analysis and machine learning algorithms. Results of an initial user study have confirmed the value of the service and indicated ways in which it should be improved.